Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Jonathan H. A. de Carvalho"'
Publikováno v:
IEEE Transactions on Computers. 71:378-389
This paper proposes a computational procedure that applies a quantum algorithm to train classical artificial neural networks. The goal of the procedure is to apply quantum walk as a search algorithm in a complete graph to find all synaptic weights of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::39b51df894bae5ec038962d8f62d6006
https://doi.org/10.1109/tc.2021.3051559
https://doi.org/10.1109/tc.2021.3051559
Publikováno v:
Intelligent Systems ISBN: 9783030917012
Adding self-loops at each vertex of a graph improves the performance of quantum walks algorithms over loopless algorithms. Many works approach quantum walks to search for a single marked vertex. In this article, we experimentally address several prob
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dd5e1ef2a5e5ee67f0bc343ca5fd1a24
https://doi.org/10.1007/978-3-030-91702-9_17
https://doi.org/10.1007/978-3-030-91702-9_17
This work proposes a computational procedure that uses a quantum walk in a complete graph to train classical artificial neural networks. The idea is to apply the quantum walk to search the weight set values. However, it is necessary to simulate a qua
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::38ac980fbe3f9ca600b78f01620680d5
Autor:
Jonathan H. A. de Carvalho, Tiago A. E. Ferreira, Fernando M. de Paula Neto, Luciano S. de Souza
Publikováno v:
Intelligent Systems ISBN: 9783030613761
BRACIS
BRACIS
The lackadaisical quantum walk is a graph search algorithm for 2D grids whose vertices have a self-loop of weight l. Since the technique depends considerably on this l, research efforts have been estimating the optimal value for different scenarios,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::44c93e77384b025f2fb47ab4b6ec9afd
https://doi.org/10.1007/978-3-030-61377-8_9
https://doi.org/10.1007/978-3-030-61377-8_9